TW202223807A - System and method of product recommendation and computer readable medium - Google Patents

Abstract

A product recommendation method and its corresponding system are provided. The product recommendation method includes: providing a dialog interface, interacting with the user through the dialog interface and receiving the user’s dialog text, extracting at least one keyword in the dialog text, calculating a recommendation priority of each keyword, and providing recommended products related to each keyword to the user according to the recommendation priority. The product recommendation method also includes: collecting each user’s at least one online product-related purchase records, advertisement clicking records and browsing records, collecting each user’s behavior records and product interaction records in a physical store, selecting a target user among these users, and providing recommended products to the target user based on the purchase records, the advertisement clicking records, the browsing records, the behavior records, and the product interaction records. The present invention further provides a computer-readable medium for performing the product recommendation method.

Description

Product recommendation system and method and computer readable medium

The present invention relates to a commodity recommendation technology, in particular to a commodity recommendation system and method based on keywords or user behavior.

In the past, companies adopted passive marketing. Although there are many services and products that can provide users with more convenience, they must wait until the user has a demand to reach the store before the store staff can introduce or recommend products to the user, resulting in the sales rate and corporate revenue cannot be large. If the rate increases, it will also lag behind other players when occupying the market, which will have an adverse impact on the human, financial, marketing and market deployment of the company.

Therefore, there is a need for a technology that can effectively, quickly, accurately and proactively provide users with more convenient services and products, so as to improve the effectiveness of product recommendation.

In order to solve the above problems, the present invention provides a product recommendation system, including: an artificial intelligence interactive module for providing a dialogue interface, and through the dialogue interface to conduct dialogue and interaction with a user to receive the user's dialogue text; and keyword recommendation A module for retrieving up to One keyword is missing, and the recommendation order of each keyword is calculated, so as to provide the user with the recommended products of each keyword according to the recommendation order.

The present invention further provides a product recommendation system, comprising: a plurality of record modules for collecting purchase records, advertisement click records and browsing records related to at least one product on the Internet of each user of the product recommendation system; a store data module, It is used to collect the behavior records and product interaction records of the users of the product recommendation system in the physical store; and the hybrid recommendation module is used to select target users among the users of the product recommendation system, and based on the purchase records , the advertisement click record, the browsing record, the behavior record and the product interaction record, and provide recommended products to the target user.

The present invention further provides a product recommendation method, comprising: providing a dialogue interface; interacting with a user through the dialogue interface to receive the user's dialogue text; retrieving at least one keyword in the dialogue text to calculate the key The recommended order of the words; and according to the recommended order, the user is provided with the recommended products of each of the keywords.

The present invention further provides a product recommendation method, which includes: collecting purchase records, advertisement click records and browsing records related to at least one product on the Internet; collecting behavior records and product interaction records of the users in physical stores; A target user is selected among the users to provide recommended products to the target user according to the purchase record, the advertisement click record, the browsing record, the behavior record and the product interaction record.

The present invention further provides a computer-readable medium, which is applied to a computing device or a computer and stores instructions for executing the above-mentioned method for recommending products.

The product recommendation system and method and the computer-readable medium of the present invention improve the overall sales method, so that store staff can no longer passively recommend products to users, but can instead actively recommend products. In addition, by clearly understanding the content of user information and user recommendations, the overall sales business can be greatly improved achievement. Furthermore, if the user cannot go to the physical store, the system pushes product recommendation information to the user, changing the original passive marketing to active marketing, which can quickly find target users, quickly occupy market segments, and improve sales strategies.

100: Product Recommendation System

101: User

102: Mobile Devices

103: Electronic Devices

104: Physical store

110: Artificial Intelligence Interactive Module

121: Purchase Recording Module

122:Ad click module

123:Browsing record module

124: Positioning module

125: Biometric Modules

126: Store data module

130,140:Database

150: Hybrid recommendation mod

151:Keyword Recommendation Module

152: User similarity recommendation module

153: Commodity similarity recommendation module

401~404: User

451~454: Commodities

601: User

651~653: Commodities

S210~S270, S310~S360, S510~S560: method steps

FIG. 1 is a schematic block diagram of a product recommendation system according to an embodiment of the present invention.

FIG. 2 is a flowchart of keyword recommendation of a product recommendation method according to an embodiment of the present invention.

FIG. 3 is a flowchart of user similarity recommendation according to a product recommendation method according to an embodiment of the present invention.

FIG. 4 is a diagram illustrating an example of user similarity recommendation according to a product recommendation method according to an embodiment of the present invention.

FIG. 5 is a flow chart of recommending product similarity in a product recommending method according to an embodiment of the present invention.

FIG. 6 is a diagram illustrating an example of product similarity recommendation according to a product recommendation method according to an embodiment of the present invention.

The embodiments of the present invention are described below by means of specific embodiments, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification.

FIG. 1 is a schematic block diagram of a product recommendation system 100 according to an embodiment of the present invention. The product recommendation system 100 includes an artificial intelligence interaction module 110 , a purchase record module 121 , an advertisement click module 122 , a browsing record module 123 , a positioning module 124 , a biometrics module 125 , a store data module 126 , and a database 130 and 140 , and a mixed recommendation module 150 , wherein the mixed recommendation module 150 includes a keyword recommendation module 151 , a user similarity recommendation module 152 , and a product similarity recommendation module 153 . The database 130 is communicatively connected to the artificial intelligence interaction module 110 and the keyword recommendation module 151 . The database 140 communicates with the purchase record module 121, the advertisement click module 122, the browsing record module 123, the positioning module 124, the biometric module 125, the store data module 126, the user similarity recommendation module 152, and the commodity Similarity recommendation module 153 .

Each module of the product recommendation system 100 in FIG. 1 can be software, hardware or firmware; if it is hardware, it can be a processing unit, processor, computer or server with data processing and computing capabilities; if For software or firmware, it may include instructions executable by a processing unit, processor, computer or server. Each module and each database of the product recommendation system 100 in FIG. 1 can be integrated in the same hardware device, or be distributed and built in a plurality of hardware devices.

The product recommendation system 100 may be affiliated to a telecommunications company or a merchant that also operates online stores and physical stores, and is used to recommend products to users. The above commodities are physical products that can be purchased (such as various electronic products or consumer products) or services that can be subscribed for (such as telecommunication services, medical services or insurance services). The artificial intelligence interaction module 110, the purchase record module 121, the advertisement click module 122, the browsing record module 123, the positioning module 124, the biometric module 125, and the store data module 126 are used for the user's mobile device and Electronic devices such as computers (such as the mobile device 102 and the electronic device 103 of the user 101 ) collect various data, and store the data in the databases 130 and 140 respectively. The group 151 , the user similarity recommendation module 152 , and the product similarity recommendation module 153 are used to execute the product recommendation methods shown in FIG. 2 , FIG. 3 and FIG. 5 respectively according to the data.

FIG. 2 is a flowchart of keyword recommendation of a product recommendation method according to an embodiment of the present invention.

First, in step S210, the user 101 can use the mobile device 102 or the electronic device 103 to open the dialogue interface provided by the artificial intelligence interactive module 110, and use text input to conduct dialogue and interaction with the artificial intelligence interactive module 110 to understand the telecommunication company or the Various products offered by merchants. The artificial intelligence interaction module 110 conducts dialogue and interaction with the user 101 through the dialogue interface through the trained artificial intelligence model.

In step S220 , the artificial intelligence interaction module 110 receives the text input by the user 101 during the dialogue interaction process (hereinafter referred to as the dialogue text) through the dialogue interface, so as to store the dialogue text in the database 130 .

Next, in step S230, the keyword recommendation module 151 obtains the dialogue text of the user 101 from the database 130, and then filters out special symbols such as punctuation marks and emoticons in the dialogue text, and filters out particle particles and exclamations in the dialogue text Words and other interference words.

In step S240, the keyword recommendation module 151 retrieves at least one keyword in the filtered dialogue text, and then calculates the occurrence frequency tf _i of each keyword i by the following formula.

The dialogue text of the user may include plural text sentences, wherein the text sent by the user each time the user clicks the transmission button of the dialogue interface during the dialogue interaction process is one text sentence. In the above formula, n _i is the number of sentences containing the keyword i in the dialogue text of the user 101 , and N is the total number of sentences in the dialogue text of the user 101 . In other words, the appearance frequency tf _i of each keyword i is calculated and generated according to the appearance ratio of each keyword i in the dialogue text of the user 101 .

Next, in step S250, the keyword recommendation module 151 calculates the importance idf _i of each keyword i in the filtered dialogue text by the following formula.

The sentences in the dialogue text of the user can be divided into plural sections, and each section includes plural sentences, and the number of sentences included in each section can be the same or different. Each of the sections can be stored in any form such as a file. In the above formula, D is the section of all dialogue texts of all users stored in the database 130 (not limited to user 101, but all users of telecommunications companies or merchants, and these users can also be regarded as all users of the product recommendation system 100). The total number of segments, and d _i is the total number of segments including the keyword i in all dialogue texts of all users stored in the database 130 . In other words, the importance idf _i of each keyword i is calculated and generated according to the appearance ratio of each keyword i in the dialogue texts of all users.

Next, in step S260, the keyword recommendation module 151 calculates the recommendation order R _i of each keyword i in the filtered dialogue text by the following formula.

R _i = tf _i × idf _i

Next, in step S270, the keyword recommendation module 151 provides and recommends at least one related product of the keyword i to the user 101 according to the recommendation order R _i of each keyword i . Specifically, in the plural keywords of the dialogue text, when the value of the recommendation order R _i of a certain keyword i is higher, the recommendation order of the related products of the keyword i is higher. The keyword recommendation module 151 can provide the above-mentioned recommended information of related products to the store staff for reference, and can also transmit the above-mentioned recommended information of related products to the mobile device 102 and/or the electronic device 103 of the user 101 .

For example, when the user 101 inputs "I want to know the latest mobile phone" during the dialogue interaction with the artificial intelligence interaction module 110, and mentions the keyword "mobile phone" many times in the subsequent dialogue interaction process, the above formula After calculation, the keyword recommendation module 151 can determine that the user 101 may be interested in the mobile phone product, and transmit the recommendation information of the mobile phone product to the mobile device 102 and/or the electronic device 103 of the user 101 .

FIG. 3 is a flowchart of user similarity recommendation according to a product recommendation method according to an embodiment of the present invention.

First, in step S310, the purchase record module 121, the advertisement click module 122, the browsing record module 123, and the store data module 126 respectively collect the purchase records, advertisement click records, and browsing records of each user of the telecommunication company or merchant and store data, and store these records and data in the database 140 . In detail, each user's purchase record is the record of the user's online purchase or application of the telecommunication company's or merchant's products. The record of each user's advertisement click is the record of the user's click on the product advertisement of the telecommunications company or merchant on the website or mobile device application. The browsing history of each user includes the records of the web pages that the user has searched and/or browsed on the Internet and are related to the products of the telecommunication company or merchant.

The store data of each user includes the user's customer attributes, as well as the user's behavior records and product interaction records in the telecommunication company's or merchant's physical stores (eg, the physical store 104 ). The customer attributes may include basic information such as the user's gender, age, income, and interests, which may be filled in by the user at the online or physical stores of the telecommunications company or business. The behavior record may include the user's inquiry of commodity information, purchase of commodities and/or application for commodities in the physical stores of the telecommunication company or merchant. Record. In brick-and-mortar stores, the behavior record can be recorded by service personnel or automated service equipment. The product interaction record may include a record of the user's interaction with the products displayed in the physical store, for example, which products the user has tried, or which products the user has watched and the viewing duration exceeds a preset value. The above-mentioned product interaction means that the user Items that may be of interest to try or watch.

In one embodiment, a camera and a sound recorder are installed in the physical store for recording images and sounds of the user in the physical store. The store data module 126 can obtain the images and voices of the users in the physical stores from the cameras and recorders of the physical stores, and store the images and voices in the database 140 . The store data module 126 can analyze the images and sounds to capture the user's product interaction records in physical stores, and then store the product interaction records into the database 140 . In addition, the store data module 126 can also analyze the images and sounds to capture the user's behavior record in the physical store, and then store the behavior record in the database 140 as a record of service personnel or automatic service equipment. Replenish.

In order to identify each user in these images and sounds, the service personnel or automatic service equipment of the physical store can record the time and place (for example, which physical store) each user arrives at the physical store, and the biometric module 125 can identify the physical store according to the time and place. Time and place to obtain the clips of each user appearing in these images and sounds, and extract each user's biometric features such as voiceprint, handwriting, face shape and gait from these clips, and then store the biometric features of each user into the data Library 140. The store data module 126 can also obtain clips of each user appearing in the images and sounds according to the time and place, identify each user in the clips according to the biometric features, and analyze the clips to extract Product interaction records and behavior records of each user.

In addition, the location module 124 is used to obtain the location information of the user. If the product recommendation system 100 is affiliated to a telecommunication company, the positioning module 124 can perform triangulation calculation according to the signal strength of the user's mobile device received by the base station of the telecommunication company, so as to obtain the user's position information, and This location information is stored in database 140 . Alternatively, regardless of whether the product recommendation system 100 is affiliated with a telecommunication company or a merchant, the location module 124 can also obtain the user's location information from the user's mobile device through the user's authorization, and store the location information in the database 140 . The location information can be used as a supplement to the time and location of each user's arrival at the physical store recorded by the service personnel or automatic service equipment of the physical store. In other words, the biometric module 125 and the store data module 126 can obtain various The clips that users appear in these images and sounds, and then capture the biometrics, product interaction records and behavior records of each user.

Next, in step S320, the user similarity recommendation module 152 selects the target user u among all the users of the telecommunication company or the merchant, and selects the commodity i to be recommended. For example, the target user u may be a user who has just purchased or applied for a product recently, and the user similarity recommendation module 152 can learn which users have recently purchased or applied for a product from the purchase records provided by the purchase record module 121 . Alternatively, the target user u may be the user targeted by the promotion. The product i to be recommended may be the product targeted by the promotion.

Next, in steps S330 to S360, the user similarity recommendation module 152 obtains the purchase records and advertisements provided by the purchase record module 121, the advertisement click module 122, the browsing record module 123 and the store data module 126 from the database 140 Click on records, browsing records and store data, input these records and data into the Collaborative Filtering model to filter, and use the User-User Similarity Matrix to find out the set of users that are similar to the target user u . The products most frequently purchased by similar users are recommended to the target user u , and the algorithm is detailed as follows.

In step S330, the user similarity recommendation module 152 generates a set S ( u ) of users with similar interests to the target user u . In one embodiment, the user similarity recommendation module 152 can use the following Jaccard index to determine whether the interests of the two users α and β are similar.

In the above Jacquard similarity formula, A and B are the collections of commodities that users α and β have had interest behaviors, respectively, where the interest behaviors refer to the fact that the user has purchased or applied for a certain commodity, clicked on the advertisement, searched or searched for the commodity. Browsing the webpage related to the product, or having a record of the product-related behavior or product interaction in the physical store. The user similarity recommendation module 152 can be informed of any purchase records, advertisement click records, browsing records and store data provided by the purchase record module 121 , the advertisement click module 122 , the browsing record module 123 and the store data module 126 . Whether a user has the above-mentioned interest behavior for any commodity, and then generates a collection of commodities that any user has an interest behavior. If the interest similarity J ( α , β ) calculated by the Jacquard similarity formula is greater than a preset value (for example, 0.5), it is determined that the interests of users α and β are similar. The user similarity recommendation module 152 can substitute the target user u and the set of commodities that each other user has had interest behaviors into the Jacquard similarity formula to calculate the interest similarity of the two users, and will make the interest similarity greater than the interest similarity. Other users of the default value join the set S ( u ).

In step S340, the user similarity recommendation module 152 generates a set N ( i ) of users who have the above-mentioned interest behaviors for the recommended product i .

In step S350, the user similarity recommendation module 152 calculates the recommendation score ρ ( u,i ) of the product i to be recommended to the target user u according to the following user similarity matrix formula.

In the above formula, v is each user in the intersection of the set S ( u ) and N ( i ), w _uv is the interest similarity between the target user u and user v , and the interest similarity can use the above-mentioned Jacquard similarity formula Calculated, r _vi is the degree of interest of user v to the item i to be recommended. The degree of interest r _vi is related to the interest behavior of user v for the product i to be recommended . The interest degree r _vi can be obtained by adding the weight values corresponding to an interest behavior.

In step S360, the user similarity recommendation module 152 determines whether to recommend and provide the commodity i to be recommended to the target user u according to the recommendation score ρ ( u,i ). Specifically, if the recommendation score ρ ( u,i ) is greater than a preset value (eg, 0.25), the user similarity recommendation module 152 recommends the item i to be recommended to the target user u . The user similarity recommendation module 152 can provide the recommendation information of the commodity i to be recommended to the store staff for reference, and can also transmit the recommendation information of the commodity i to be recommended to the mobile device and/or electronic device of the target user u . Conversely, if the recommendation score ρ ( u,i ) is less than or equal to the preset value, the user similarity recommendation module 152 does not recommend the product i to be recommended to the target user u , and the user similarity recommendation module 152 can select another product to be recommended. The product repeats the above steps.

FIG. 4 is an exemplary diagram of user similarity recommendation according to a product recommendation method according to an embodiment of the present invention, wherein the solid line represents "purchase", and the dotted line represents "recommendation". As shown in FIG. 4 , user 401 purchased commodities 451 , 452 and 454 , user 402 purchased commodity 454 , user 403 purchased commodity 453 , and target user 404 purchased commodities 451 and 452 . After the above process, the user similarity recommendation module 152 determines that the interests of the user 401 and the target user 404 are similar, and selects the product 454 purchased by the user 401 and not purchased by the target user 404 as the product to be recommended. The recommendation score of the target user 404 is greater than the preset value, so the user similarity recommendation module 152 recommends the product 454 to the target user 404 .

FIG. 5 is a flow chart of recommending product similarity in a product recommending method according to an embodiment of the present invention.

First, in step S510, the purchase record module 121, the advertisement click module 122, the browsing record module 123, and the store data module 126 respectively collect each user of the telecommunication company or the merchant purchase records, advertisement click records, browsing records and store data, and store these records and data in the database 140 . Step S510 is the same as step S310 in FIG. 3 , and details are not repeated here.

Next, in step S520, the commodity similarity recommendation module 153 selects the target user u among all the users of the telecommunication company or the merchant.

Next, in steps S530 to S560, the product similarity recommendation module 153 obtains the purchase records and advertisements provided by the purchase record module 121, the advertisement click module 122, the browsing record module 123 and the store data module 126 from the database 140 Click records, browsing records and store data, input these records and data into the Collaborative Filtering model to filter, and use the Item-Item Similarity Matrix to find out the products similar to those recently browsed or purchased by the target user u After the collection of products, similar products are recommended to the target user u , and the algorithm is detailed as follows.

In step S530, the commodity similarity recommendation module 153 generates a set M ( u ) of commodities in which the target user u has an interest behavior. As described above, the product similarity recommendation module 153 can be based on the purchase records, advertisement click records, browsing records and store records provided by the purchase record module 121 , the advertisement click module 122 , the browsing record module 123 and the store data module 126 . The data knows whether any user has any interest behavior on any commodity, and then generates a collection of commodities that any user has an interest behavior in.

In step S540, the product similarity recommendation module 153 generates a set S ( j ) of products similar to the product j purchased by the target user u . Here, the similarity of two products means that the two products have a common label. In detail, each product can have multiple labels to indicate the brand, origin, and whether it is an organic product. If the value is greater than the preset value (for example, 0.5), the product similarity recommendation module 153 determines that the two products are similar. In this way, the product similarity recommendation module 153 can generate a product similar to the product j purchased by the target user u . The set S ( j ) of .

In another embodiment, the above-mentioned commodity j can also be a commodity that the target user u has had an interest behavior (such as browsing or purchasing, etc.), so the set S ( j ) can also be a commodity that the target user u has had an interest behavior in the past. A collection of commodities that are similar to commodity j .

In step S550, the product similarity recommendation module 153 calculates the recommendation score P ( u,i ) of the product to be recommended according to the following product similarity matrix formula.

In the above formula, commodity i is each commodity in the intersection of set M ( u ) and set S ( j ), w _ij is the similarity between commodity i and commodity j , r _ui is the target user u for commodity i of interest. The commodity similarity recommendation module 153 can use the following formula to calculate the similarity w _ij between commodity i and commodity j . The calculation of the interest degree r _ui can be compared with the aforementioned interest degree r _vi .

In the above formula, N ( i ) and N ( j ) are the sets of users who have been interested in commodity i and commodity j , respectively.

In step S560, the product similarity recommendation module 153 determines whether to provide the recommended product i to the target user u according to the recommendation score P ( u,i ). In detail, if the recommendation score P ( u,i ) is greater than the preset value (eg 0.25), the product similarity recommendation module 153 recommends the intersection of the set M ( u ) and the set S ( j ) to the target user u each commodity i . The product similarity recommendation module 153 can provide the recommended information of the product i to the store staff for reference, and can also transmit the recommended information of the product i to the mobile device and/or electronic device of the target user u . Conversely, if the recommendation score P ( u,i ) is less than or equal to the preset value, the product similarity recommendation module 153 does not recommend the product i to the target user u .

FIG. 6 is a diagram illustrating an example of product similarity recommendation according to a product recommendation method according to an embodiment of the present invention, wherein the solid line represents “purchase”, and the dotted line represents “recommendation”. As shown in FIG. 6 , the target user 601 has purchased commodities 651 and 652 . The commodity similarity recommendation module 153 recommends commodities 653 similar to commodities 651 and 652 to the target user 601 after the above calculation. For example, commodities 651 and 652 are a computer and a keyboard, respectively, and commodity 653 is a mouse.

In addition, the present invention also discloses a computer-readable medium, which is applied to a computing device or computer having a processor (eg, CPU, GPU, etc.) and/or memory, and stores instructions, and can utilize the computing device or computer. The computer executes the computer-readable medium through a processor and/or a memory, so as to execute the above-mentioned methods and steps when executing the computer-readable medium.

To sum up, the product recommendation system and method and the computer-readable medium proposed by the present invention have the following features and effects:

1. The artificial intelligence interactive module can analyze the semantic intention of the user, and analyze the products that the user may be interested in and the service information that the user may need through the dialogue keywords. The product recommendation system and method of the present invention can recommend products accordingly. This method increases the success rate of selling products.

2. If the customer has purchased or applied for a product, the product recommendation system and method of the present invention can use the user's purchase record, advertisement click record, browsing record, and store data to analyze and calculate the similarity between users and products, and recommend similar users. The purchased products or other products that are most similar to the current products are recommended for cross-selling, and after being converted into a user group with similar interests, the products they buy most often are viewed and recommended to the currently locked target users. .

3. The product recommendation system and method of the present invention can obtain the user's location information, so as to analyze the user's past location and the store where the user may consume, and combine the user's browsing records and biometric data to find the most similar product to the currently browsed product. Product groups, and then recommend them to users.

4. Store personnel can obtain real-time user needs from the product recommendation system and method of the present invention, so as to avoid recommending unsuitable products to users, resulting in poor user experience and affecting service quality. On the contrary, the sales rate of products can be improved in this way. , and increase overall revenue.

The above-mentioned embodiments are only used to illustrate the principle and effect of the present invention, but are not intended to limit the present invention. Anyone with ordinary knowledge in the technical field can modify and change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention should be as listed in the patent application scope described later.

100: Product Recommendation System

101: User

102: Mobile Devices

103: Electronic Devices

104: Physical store

110: Artificial Intelligence Interactive Module

121: Purchase Recording Module

122:Ad click module

123:Browsing record module

124: Positioning module

125: Biometric Modules

126: Store data module

130,140:Database

150: Hybrid recommendation mod

151:Keyword Recommendation Module

152: User similarity recommendation module

153: Commodity similarity recommendation module

Claims (11)

A product recommendation system, including: an artificial intelligence interactive module, used for providing a dialogue interface, and through the dialogue interface, it interacts with the user through dialogue, so as to receive the dialogue text of the user; and The keyword recommendation module is used for retrieving at least one keyword in the dialogue text, and calculating the recommendation order of each keyword, so as to provide the user with the recommended products of each keyword according to the recommendation order. The product recommendation system according to claim 1, wherein the keyword recommendation module calculates the frequency of occurrence of each keyword according to the appearance ratio of each keyword in the dialogue text of the user, and calculates the frequency of occurrence of each keyword according to the ratio of each keyword in the dialogue text of the user, and The importance of each keyword is calculated based on the proportion of words appearing in the dialogue texts of all users of the product recommendation system, and then the recommended order of each keyword is calculated according to the frequency of occurrence and the importance. The product recommendation system as described in claim 1, further comprising: Multiple record modules are used to collect purchase records, advertisement click records and browsing records related to at least one product on the Internet for each user of the product recommendation system; The store data module is used to collect the behavior records and product interaction records of each user of the product recommendation system in the physical store; and The user similarity recommendation module is used to select a target user among the users of the product recommendation system, so as to report to the target user according to the purchase record, the advertisement click record, the browsing record, the behavior record and the product interaction record. The user provides the recommended product that a user with similar interests to the target user has had an interest behavior. The product recommendation system as described in claim 1, further comprising: Multiple record modules are used to collect purchase records, advertisement click records and browsing records related to at least one product on the Internet for each user of the product recommendation system; The store data module is used to collect the behavior records and product interaction records of each user of the product recommendation system in the physical store; and The product similarity recommendation module is used to select a target user among the users of the product recommendation system, so as to report to the target user according to the purchase record, the advertisement click record, the browsing record, the behavior record and the product interaction record. The user provides the recommended product that is similar to the product that the target user has had an interest in. A product recommendation system, including: Multiple record modules are used to collect purchase records, advertisement click records and browsing records related to at least one product on the Internet for each user of the product recommendation system; The store data module is used to collect the behavior records and product interaction records of each user of the product recommendation system in the physical store; and The hybrid recommendation module is used to select a target user among the users of the product recommendation system to provide the target user with the purchase record, the advertisement click record, the browsing record, the behavior record and the product interaction record. Recommended product. The product recommendation system according to claim 5, wherein the hybrid recommendation module includes: The user similarity recommendation module is used to provide the target user with information about the user's interest behavior similar to the target user's interest based on the purchase record, the advertisement click record, the browsing record, the behavior record and the product interaction record. the recommended product; and The product similarity recommendation module is used to provide the target user with products similar to the target user's interest behavior based on the purchase record, the advertisement click record, the browsing record, the behavior record and the product interaction record. The recommended product. A product recommendation method, including: provide a dialogue interface; Dialogue and interaction with the user through the dialogue interface to receive the user's dialogue text; extracting at least one keyword in the conversation text to calculate the recommendation order of each keyword; and According to the recommendation order, the user is provided with the recommended products of each keyword. The product recommendation method according to claim 7, wherein the step of calculating the recommendation order of each keyword comprises: Calculate the frequency of occurrence of each keyword according to the proportion of the keyword in the dialogue text of the user; Calculate the importance of each keyword according to the proportion of each keyword appearing in the dialogue texts of all users of the product recommendation system; and The recommended order of each of the keywords is calculated according to the frequency of occurrence and the importance. A product recommendation method, including: Collect purchase records, advertisement click records and browsing records related to at least one commodity on the Internet; Collect the behavior records and product interaction records of each user in the physical store; and A target user is selected among the users to provide recommended products to the target user according to the purchase record, the advertisement click record, the browsing record, the behavior record and the product interaction record. The product recommendation method according to claim 9, wherein the step of providing recommended products to the target user includes: According to the purchase record, the advertisement click record, the browsing record, the behavior record and the product interaction record, provide the target user with the recommended product that is similar to the target user's interest and has been interested in behavior; and According to the purchase record, the advertisement click record, the browsing record, the behavior record and the product interaction record, provide the target user with the recommended product that is similar to the product that the target user has been interested in. A computer-readable medium, applied to a computing device or a computer, and storing instructions to execute the method for recommending products as described in any one of claims 7 to 10.

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